


"""simple docstring"""
from __future__ import annotations

def 				_snake_case      (   __snake_case							:      list[list[int]]   ):




   """simple docstring"""







   _lowerCamelCase	:					Dict   =		len(__snake_case   )
   # We need to create solution object to save path.
   _lowerCamelCase	:					int   =		[[0 for _ in range(__snake_case   )] for _ in range(__snake_case   )]
   _lowerCamelCase	:					str   =		run_maze(__snake_case						, 0						, 0						, __snake_case   )
   if solved:
      print("""\n""".join(str(__snake_case   ) for row in solutions   )   )
   else:
      print("""No solution exists!"""   )
   return solved

def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int						, __snake_case							:      int						, __snake_case							:      list[list[int]]   ):




   """simple docstring"""







   _lowerCamelCase	:					Optional[Any]   =		len(__snake_case   )
   # Final check point.
   if i == j == (size - 1):
      _lowerCamelCase	:					List[str]   =		1
      return True

   _lowerCamelCase	:					List[Any]   =		(not i < 0) and (not j < 0)  # Check lower bounds
   _lowerCamelCase	:					Union[str, Any]   =		(i < size) and (j < size)  # Check upper bounds

   if lower_flag and upper_flag:
      # check for already visited and block points.
      _lowerCamelCase	:					List[str]   =		(not solutions[i][j]) and (not maze[i][j])
      if block_flag:
         # check visited
         _lowerCamelCase	:					Union[str, Any]   =		1

         # check for directions
         if (
             run_maze(__snake_case						, i + 1						, __snake_case						, __snake_case   )
             or run_maze(__snake_case						, __snake_case						, j + 1						, __snake_case   )
             or run_maze(__snake_case						, i - 1						, __snake_case						, __snake_case   )
             or run_maze(__snake_case						, __snake_case						, j - 1						, __snake_case   )
         ):
            return True

         _lowerCamelCase	:					str   =		0
         return False
   return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()




import math

import tensorflow as tf
from packaging import version






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0		)     ,				x.dtype		)		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(math.pi     ,				x.dtype		)
							A      :					int      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					Tuple      		=					0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi		) * (x + coeff * tf.pow(lowerCamelCase_     ,				3		))		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					Union[str, Any]      		=					tf.convert_to_tensor(lowerCamelCase_		)

							return x * tf.tanh(tf.math.softplus(lowerCamelCase_		)		)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					List[Any]      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					str      		=					tf.cast(0.7_978_845_608     ,				x.dtype		)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)		))






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(1.702     ,				x.dtype		)
							return x * tf.math.sigmoid(coeff * x		)






def        snake_case__		(    lowerCamelCase_		):
							return tf.clip_by_value(_gelu(lowerCamelCase_		)     ,				-10     ,				10		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=-1		):
							A		,		A      :					Dict      		=					tf.split(lowerCamelCase_     ,				2     ,				axis=lowerCamelCase_		)
							return a * tf.math.sigmoid(lowerCamelCase_		)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def        snake_case__		(    lowerCamelCase_		):
									return tf.keras.activations.gelu(lowerCamelCase_     ,				approximate=lowerCamelCase_		)

		lowercase			:				Tuple   =     tf.keras.activations.gelu

		lowercase			:				Union[str, Any]   =     approximate_gelu_wrap
else:
		lowercase			:				str   =     _gelu
		lowercase			:				Dict   =     _gelu_new


lowercase			:				Union[str, Any]   =     {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def        snake_case__		(    lowerCamelCase_		):
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}'		)






